In [15]:
## numeric columns
for col in num_col:
sns.histplot(hr[col],kde=True)
plt.show()
## Import the library
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from ydata_profiling import ProfileReport
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
plt.style.use('fivethirtyeight')
sns.set()
pd.options.display.float_format = '{:,.2f}'.format
pd.options.display.max_rows = None
pd.options.display.max_columns = None
hr=pd.read_csv("HR-Employee-Attrition.csv")
hr.sample(10)
| Age | Attrition | BusinessTravel | DailyRate | Department | DistanceFromHome | Education | EducationField | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | Gender | HourlyRate | JobInvolvement | JobLevel | JobRole | JobSatisfaction | MaritalStatus | MonthlyIncome | MonthlyRate | NumCompaniesWorked | Over18 | OverTime | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 585 | 23 | Yes | Travel_Rarely | 1243 | Research & Development | 6 | 3 | Life Sciences | 1 | 811 | 3 | Male | 63 | 4 | 1 | Laboratory Technician | 1 | Married | 1601 | 3445 | 1 | Y | Yes | 21 | 4 | 3 | 80 | 2 | 1 | 2 | 3 | 0 | 0 | 0 | 0 |
| 480 | 30 | Yes | Travel_Frequently | 448 | Sales | 12 | 4 | Life Sciences | 1 | 648 | 2 | Male | 74 | 2 | 1 | Sales Representative | 1 | Married | 2033 | 14470 | 1 | Y | No | 18 | 3 | 3 | 80 | 1 | 1 | 2 | 4 | 1 | 0 | 0 | 0 |
| 142 | 38 | No | Travel_Rarely | 364 | Research & Development | 3 | 5 | Technical Degree | 1 | 193 | 4 | Female | 32 | 3 | 2 | Research Scientist | 3 | Single | 4317 | 2302 | 3 | Y | Yes | 20 | 4 | 2 | 80 | 0 | 19 | 2 | 3 | 3 | 2 | 2 | 2 |
| 736 | 48 | No | Travel_Rarely | 1355 | Research & Development | 4 | 4 | Life Sciences | 1 | 1024 | 3 | Male | 78 | 2 | 3 | Healthcare Representative | 3 | Single | 10999 | 22245 | 7 | Y | No | 14 | 3 | 2 | 80 | 0 | 27 | 3 | 3 | 15 | 11 | 4 | 8 |
| 441 | 42 | No | Travel_Frequently | 1474 | Research & Development | 5 | 2 | Other | 1 | 591 | 2 | Male | 97 | 3 | 1 | Laboratory Technician | 3 | Married | 2093 | 9260 | 4 | Y | No | 17 | 3 | 4 | 80 | 1 | 8 | 4 | 3 | 2 | 2 | 2 | 0 |
| 1335 | 39 | No | Travel_Rarely | 835 | Research & Development | 19 | 4 | Other | 1 | 1871 | 4 | Male | 41 | 3 | 2 | Research Scientist | 4 | Divorced | 3902 | 5141 | 8 | Y | No | 14 | 3 | 2 | 80 | 3 | 7 | 2 | 3 | 2 | 2 | 2 | 2 |
| 1081 | 35 | No | Travel_Rarely | 1029 | Research & Development | 16 | 3 | Life Sciences | 1 | 1529 | 4 | Female | 91 | 2 | 3 | Healthcare Representative | 2 | Single | 8606 | 21195 | 1 | Y | No | 19 | 3 | 4 | 80 | 0 | 11 | 3 | 1 | 11 | 8 | 3 | 3 |
| 1371 | 56 | No | Travel_Rarely | 1443 | Sales | 11 | 5 | Marketing | 1 | 1935 | 4 | Female | 89 | 2 | 2 | Sales Executive | 1 | Married | 5380 | 20328 | 4 | Y | No | 16 | 3 | 3 | 80 | 1 | 6 | 3 | 3 | 0 | 0 | 0 | 0 |
| 971 | 51 | No | Travel_Rarely | 1405 | Research & Development | 11 | 2 | Technical Degree | 1 | 1367 | 4 | Female | 82 | 2 | 4 | Manufacturing Director | 2 | Single | 13142 | 24439 | 3 | Y | No | 16 | 3 | 2 | 80 | 0 | 29 | 1 | 2 | 5 | 2 | 0 | 3 |
| 76 | 35 | No | Travel_Rarely | 776 | Sales | 1 | 4 | Marketing | 1 | 100 | 3 | Male | 32 | 2 | 2 | Sales Executive | 1 | Single | 4312 | 23016 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 16 | 2 | 3 | 15 | 13 | 2 | 8 |
hr.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1470 entries, 0 to 1469 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 1470 non-null int64 1 Attrition 1470 non-null object 2 BusinessTravel 1470 non-null object 3 DailyRate 1470 non-null int64 4 Department 1470 non-null object 5 DistanceFromHome 1470 non-null int64 6 Education 1470 non-null int64 7 EducationField 1470 non-null object 8 EmployeeCount 1470 non-null int64 9 EmployeeNumber 1470 non-null int64 10 EnvironmentSatisfaction 1470 non-null int64 11 Gender 1470 non-null object 12 HourlyRate 1470 non-null int64 13 JobInvolvement 1470 non-null int64 14 JobLevel 1470 non-null int64 15 JobRole 1470 non-null object 16 JobSatisfaction 1470 non-null int64 17 MaritalStatus 1470 non-null object 18 MonthlyIncome 1470 non-null int64 19 MonthlyRate 1470 non-null int64 20 NumCompaniesWorked 1470 non-null int64 21 Over18 1470 non-null object 22 OverTime 1470 non-null object 23 PercentSalaryHike 1470 non-null int64 24 PerformanceRating 1470 non-null int64 25 RelationshipSatisfaction 1470 non-null int64 26 StandardHours 1470 non-null int64 27 StockOptionLevel 1470 non-null int64 28 TotalWorkingYears 1470 non-null int64 29 TrainingTimesLastYear 1470 non-null int64 30 WorkLifeBalance 1470 non-null int64 31 YearsAtCompany 1470 non-null int64 32 YearsInCurrentRole 1470 non-null int64 33 YearsSinceLastPromotion 1470 non-null int64 34 YearsWithCurrManager 1470 non-null int64 dtypes: int64(26), object(9) memory usage: 402.1+ KB
hr.duplicated().sum()
0
hr.isnull().sum()
Age 0 Attrition 0 BusinessTravel 0 DailyRate 0 Department 0 DistanceFromHome 0 Education 0 EducationField 0 EmployeeCount 0 EmployeeNumber 0 EnvironmentSatisfaction 0 Gender 0 HourlyRate 0 JobInvolvement 0 JobLevel 0 JobRole 0 JobSatisfaction 0 MaritalStatus 0 MonthlyIncome 0 MonthlyRate 0 NumCompaniesWorked 0 Over18 0 OverTime 0 PercentSalaryHike 0 PerformanceRating 0 RelationshipSatisfaction 0 StandardHours 0 StockOptionLevel 0 TotalWorkingYears 0 TrainingTimesLastYear 0 WorkLifeBalance 0 YearsAtCompany 0 YearsInCurrentRole 0 YearsSinceLastPromotion 0 YearsWithCurrManager 0 dtype: int64
hr.nunique()
Age 43 Attrition 2 BusinessTravel 3 DailyRate 886 Department 3 DistanceFromHome 29 Education 5 EducationField 6 EmployeeCount 1 EmployeeNumber 1470 EnvironmentSatisfaction 4 Gender 2 HourlyRate 71 JobInvolvement 4 JobLevel 5 JobRole 9 JobSatisfaction 4 MaritalStatus 3 MonthlyIncome 1349 MonthlyRate 1427 NumCompaniesWorked 10 Over18 1 OverTime 2 PercentSalaryHike 15 PerformanceRating 2 RelationshipSatisfaction 4 StandardHours 1 StockOptionLevel 4 TotalWorkingYears 40 TrainingTimesLastYear 7 WorkLifeBalance 4 YearsAtCompany 37 YearsInCurrentRole 19 YearsSinceLastPromotion 16 YearsWithCurrManager 18 dtype: int64
for col in hr.columns:
# Check column dtype
if hr[col].dtype == 'object':
# Column details
print(f"Column: {col}")
# Number of unique values
print(f"Unique values: {hr[col].unique()}")
# Value counts
print(hr[col].value_counts())
# Separator
print("="*40)
Column: Attrition Unique values: ['Yes' 'No'] No 1233 Yes 237 Name: Attrition, dtype: int64 ======================================== Column: BusinessTravel Unique values: ['Travel_Rarely' 'Travel_Frequently' 'Non-Travel'] Travel_Rarely 1043 Travel_Frequently 277 Non-Travel 150 Name: BusinessTravel, dtype: int64 ======================================== Column: Department Unique values: ['Sales' 'Research & Development' 'Human Resources'] Research & Development 961 Sales 446 Human Resources 63 Name: Department, dtype: int64 ======================================== Column: EducationField Unique values: ['Life Sciences' 'Other' 'Medical' 'Marketing' 'Technical Degree' 'Human Resources'] Life Sciences 606 Medical 464 Marketing 159 Technical Degree 132 Other 82 Human Resources 27 Name: EducationField, dtype: int64 ======================================== Column: Gender Unique values: ['Female' 'Male'] Male 882 Female 588 Name: Gender, dtype: int64 ======================================== Column: JobRole Unique values: ['Sales Executive' 'Research Scientist' 'Laboratory Technician' 'Manufacturing Director' 'Healthcare Representative' 'Manager' 'Sales Representative' 'Research Director' 'Human Resources'] Sales Executive 326 Research Scientist 292 Laboratory Technician 259 Manufacturing Director 145 Healthcare Representative 131 Manager 102 Sales Representative 83 Research Director 80 Human Resources 52 Name: JobRole, dtype: int64 ======================================== Column: MaritalStatus Unique values: ['Single' 'Married' 'Divorced'] Married 673 Single 470 Divorced 327 Name: MaritalStatus, dtype: int64 ======================================== Column: Over18 Unique values: ['Y'] Y 1470 Name: Over18, dtype: int64 ======================================== Column: OverTime Unique values: ['Yes' 'No'] No 1054 Yes 416 Name: OverTime, dtype: int64 ========================================
for col in hr.columns:
# Check column numiric
if hr[col].dtype != 'object':
# Column details
print(f"Column: {col}")
# max & min values
print(f"max: {hr[col].max()}",f"min: {hr[col].min()}")
# Separator
print("="*40)
Column: Age max: 60 min: 18 ======================================== Column: DailyRate max: 1499 min: 102 ======================================== Column: DistanceFromHome max: 29 min: 1 ======================================== Column: Education max: 5 min: 1 ======================================== Column: EmployeeCount max: 1 min: 1 ======================================== Column: EmployeeNumber max: 2068 min: 1 ======================================== Column: EnvironmentSatisfaction max: 4 min: 1 ======================================== Column: HourlyRate max: 100 min: 30 ======================================== Column: JobInvolvement max: 4 min: 1 ======================================== Column: JobLevel max: 5 min: 1 ======================================== Column: JobSatisfaction max: 4 min: 1 ======================================== Column: MonthlyIncome max: 19999 min: 1009 ======================================== Column: MonthlyRate max: 26999 min: 2094 ======================================== Column: NumCompaniesWorked max: 9 min: 0 ======================================== Column: PercentSalaryHike max: 25 min: 11 ======================================== Column: PerformanceRating max: 4 min: 3 ======================================== Column: RelationshipSatisfaction max: 4 min: 1 ======================================== Column: StandardHours max: 80 min: 80 ======================================== Column: StockOptionLevel max: 3 min: 0 ======================================== Column: TotalWorkingYears max: 40 min: 0 ======================================== Column: TrainingTimesLastYear max: 6 min: 0 ======================================== Column: WorkLifeBalance max: 4 min: 1 ======================================== Column: YearsAtCompany max: 40 min: 0 ======================================== Column: YearsInCurrentRole max: 18 min: 0 ======================================== Column: YearsSinceLastPromotion max: 15 min: 0 ======================================== Column: YearsWithCurrManager max: 17 min: 0 ========================================
hr.columns
Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',
'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
'YearsWithCurrManager'],
dtype='object')
columns_to_drop = ['EmployeeCount','EmployeeNumber','Over18','StandardHours']
# Drop columns
for col in columns_to_drop:
hr.drop(col, axis=1, inplace=True)
obj=hr.select_dtypes(include="object")
num=hr.select_dtypes(exclude="object")
obj_col=list(hr.select_dtypes(include="object").columns)
num_col=list(hr.select_dtypes(exclude="object").columns)
## numeric columns
for col in num_col:
sns.histplot(hr[col],kde=True)
plt.show()
## object columns
for i in obj_col:
fig=px.bar(data_frame=hr[i].value_counts(),text_auto=True)
fig.update_layout(title="Distribution of " + i)
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
## Relation between Attrition and object columns
col=['BusinessTravel',
'Department',
'EducationField',
'Gender',
'JobRole',
'MaritalStatus',
'OverTime']
for i in col:
ee=hr.groupby(i)['Attrition'].count().reset_index().sort_values('Attrition',ascending=False)
fig = px.bar(ee, x=i,y="Attrition",text_auto=True)
fig.update_layout(title="Relation between Attrition and " + i)
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
## Relation between Attrition and Numiric Columns
col=['Age','DistanceFromHome','Education','EnvironmentSatisfaction','JobInvolvement','JobLevel','JobSatisfaction','NumCompaniesWorked',
'PercentSalaryHike','PerformanceRating','RelationshipSatisfaction','StockOptionLevel','TotalWorkingYears','TrainingTimesLastYear',
'WorkLifeBalance','YearsAtCompany','YearsInCurrentRole','YearsSinceLastPromotion','YearsWithCurrManager']
for i in col:
ee=hr.groupby(i)['Attrition'].count().reset_index().sort_values('Attrition',ascending=False)
fig = px.bar(ee, x=i,y="Attrition",text_auto=True)
fig.update_layout(title="Relation between Attrition and " + i)
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
obj_col
['Attrition', 'BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'OverTime']
col=['Department','EducationField','Gender','JobRole','MaritalStatus','OverTime']
for i in col:
be=hr.groupby([i,'BusinessTravel'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='BusinessTravel',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and BusinessTravel')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
col=['BusinessTravel','EducationField','Gender','JobRole','MaritalStatus','OverTime']
for i in col:
be=hr.groupby([i,'Department'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='Department',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and Department')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
col=['BusinessTravel','Department','Gender','JobRole','MaritalStatus','OverTime']
for i in col:
be=hr.groupby([i,'EducationField'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='EducationField',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and EducationField')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
col=['BusinessTravel','Department','EducationField','JobRole','MaritalStatus','OverTime']
for i in col:
be=hr.groupby([i,'Gender'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='Gender',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and Gender')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
col=['BusinessTravel','Department','EducationField','Gender','MaritalStatus','OverTime']
for i in col:
be=hr.groupby([i,'JobRole'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='JobRole',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and JobRole')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
col=[
'BusinessTravel',
'Department',
'EducationField',
'Gender',
'JobRole',
'OverTime']
for i in col:
be=hr.groupby([i,'MaritalStatus'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='MaritalStatus',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and MaritalStatus')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()
col=['BusinessTravel','Department','EducationField','Gender','JobRole','MaritalStatus']
for i in col:
be=hr.groupby([i,'OverTime'])["Attrition"].count().reset_index().sort_values('Attrition',ascending=False)
fig=px.bar(data_frame=be,x=i,y="Attrition",color='OverTime',barmode='group',text_auto=True)
fig.update_layout(title="Attrition with " + i + ' and OverTime')
fig.update_layout(xaxis_title= i)
fig.update_traces(textposition='outside')
fig.update_layout(title_x=.5)
fig.show()